Paper Info
Title
FaceCAPTCHA: a CAPTCHA that identifies the gender of face images unrecognized by existing gender classifiers
Abstract
Computers tend to fail to classify human faces by gender, especially upon changes in viewpoint or upon occlusion that make it more difficult to extract the necessary image features. In contrast, humans are good at identifying gender but have difficulties in dealing with a large number of images. Accounting for this gap, we proposed FaceCAPTCHA, a novel image-based CAPTCHA that asks users to identify the gender of face images whose gender cannot be recognized by computers (gender-indiscernible faces). By converting the manual gender classification task into a CAPTCHA test, FaceCAPTCHA was designed to not only continuously identify the gender of gender-indiscernible faces but also differentiate between humans and computers and generate new test images. Our user studies showed that FaceCAPTCHA reliably identifies gender-indiscernible faces. A single eight-image FaceCAPTCHA test was completed in 12.41 s on average with a human success rate of 86.51 %, which can be further increased by filtering error-prone test images. In contrast, the probability of passing a FaceCAPTCHA test by random guessing was 0.006 %. We could therefore conclude that FaceCAPTCHA is robust against malicious attacks and easy enough for practical use.
Year
DOI
Venue
2014
10.1007/s11042-013-1422-z
Multimedia Tools and Applications
Keywords
Field
DocType
CAPTCHA,Crowdsourcing,Gender classification,Human computation,Image tagging,Web application
Computer vision,Pattern recognition,Feature (computer vision),Crowdsourcing,Computer science,Filter (signal processing),Human computation,Artificial intelligence,CAPTCHA,Web application,User studies
Journal
Volume
Issue
ISSN
72
2
1380-7501
Citations 
PageRank 
References 
1
0.35
31
Authors
5
Name
Order
Citations
PageRank
Jonghak Kim141.08
Sang Tae Kim252.91
Joonhyuk Yang3161.77
Jung-hee Ryu410812.98
KwangYun Wohn530942.24